IJE Advance Access published online on April 3, 2008
International Journal of Epidemiology, doi:10.1093/ije/dyn059
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Body mass index, weight change and mortality risk in a prospective study in India
1 Screening Group, International Agency for Research on Cancer, Lyon, France.
2 Division of Radiation Oncology, Regional Cancer Center, Medical College Campus, Trivandrum, India.
3 Division of Preventive Oncology, Regional Cancer Center, Medical College Campus, Trivandrum, India.
4 Trivandrum Oral Cancer Screening Project, Regional Cancer Centre, Trivandrum, India.
5 Division of Cytopathology, Regional Cancer Center, Medical College Campus, Trivandrum, India.
* Corresponding author. Screening Group, International Agency for Research on Cancer, 69372 Lyon cedex 08, France. E-mail: sauvagetc{at}iarc.fr
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Abstract |
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Background Although the detrimental effect of overweight and obesity has been extensively reported in Western populations, little is known on the association between body weight, weight change and mortality in Asian populations whose weight distribution and mortality differ considerably from the West.
Methods A cohort of 75 868 subjects aged 35 years and above, participants of the Trivandrum Oral Cancer Study—a cluster-randomized controlled trial originally implemented to evaluate the efficacy of visual inspection on oral cancer, in Kerala State, South India—were followed up from 1995 to 2004. Weight and height were measured both at baseline and in 3.5-year follow-up surveys. Early years of follow-up were excluded from the analyses. Relative risks of overall death and cause-specific death were estimated according to the body mass index (BMI) category of the WHO Asian population definitions, and to weight changes between two surveys.
Results Low BMI was a predictor of mortality, while high BMI was not. Mortality risks in men adjusted for age, smoking habits and other potential confounders, as compared with a BMI 18.5–22.9 kg/m2, were 1.26 (95% CI 1.03–1.55) for BMI < 16 kg/m2; 1.16 (1.03–1.32) for BMI = 16–18.4 kg/m2; 0.95 (0.81–1.12) for BMI = 23–24.9 kg/m2; 0.85 (0.69–1.05) for BMI = 25–27.4 kg/m2; and 0.89 (0.65–1.21) for BMI 
27.5 kg/m2. Similar findings were observed in women. BMI was not associated with deaths from cancer, cardiovascular and cerebrovascular diseases, and diabetes. A low BMI (<16 kg/m2) was associated with increased deaths from chronic respiratory diseases. Smoking and socio-economical status did modify the association. A moderate weight gain of 4–10% between the two surveys was associated with decreased risk of death, while moderate and severe weight loss were predictive factors of death. Similar results were observed in both men and women.
Conclusions Among this Indian rural population, mild to severe leanness (BMI < 16 kg/m2) and weight loss were important determinants of mortality, especially from chronic respiratory diseases, while overweight and above (BMI > 23 kg/m2) did not show any detrimental effect.
Keywords Body mass index, body weight change, mortality, neoplasm, prospective studies, India
Accepted 27 February 2008
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Introduction |
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Anthropometric measurements, such as body weight, are good markers of the nutritional status of individuals and communities. The body mass index (BMI), defined as the weight in kilograms divided by the square of the height in metres, is a simple and useful index of relative weight, applied to assess obesity or chronic energy deficiency.1 The association between BMI and mortality has been extensively reported,2–5 the majority of evidence coming from Western countries, where both overweight and obesity are associated with decreased longevity and are major public health issues.6,7 However, little is known about this association for other populations with different environmental and genetic backgrounds.
The BMI distribution among Asian populations tends to shift towards the low level, with a high proportion of underweight and a small proportion of overweight and obese persons. In India, overweight and obesity is more prevalent among affluent persons living in large cities,8 and underweight more frequent in rural populations (40% with a BMI < 18.5 kg/m2), where obesity is scarce (0.5%).9,10 The association between low BMI and mortality has been scarcely reported, although leanness is a general feature of persons from low-resource countries.9 A recent study performed in Northern rural India showed increased morbidity rates among the under- and over-weight,11 described as the twin burden of under-nutrition and over-nutrition.12
We implemented a large population-based cohort study in southern rural India, assessing the BMI both at baseline and at the follow-up survey 3 years later. We accounted for potential confounding effects of tobacco use,13 socio-economic level and early deaths. Study participants were actively followed-up during a mean duration of 8 years, to examine whether both thinness and overweight were independent predictors of mortality in a U-shaped relationship. Also, we examined the detrimental effects of weight loss or gain during the two surveys.
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Methods |
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The data presented here were collected from an on-going cluster-randomized community-based oral cancer screening trial implemented to evaluate the efficacy of oral visual inspection on the incidence and mortality of oral cancer. The study, described elsewhere,14,15 was carried out in 13 clusters, called Panchayaths, or municipal administrative units, in Trivandrum district, Kerala, South India (Appendix 1). At baseline, 114 601 eligible subjects aged 35 and older were randomly assigned to an intervention group (59 894 subjects from seven clusters) or a control group (54 707 subjects from six clusters). Out of the eligible subjects, 88 910 participated to the study (78% participation rate) and were followed-up for cancer incidence and mortality. The study protocol was reviewed and approved by the Scientific and Human Ethics Committees of the Regional Cancer Centre, Trivandrum and the International Agency for Research on Cancer, Lyon, France. A written informed consent was obtained from each participant.
All participants answered a lifestyle questionnaire and had anthropometric measurements taken at baseline (Round 1) and 3.5 years later (Round 2). Data were collected through house visits by trained health workers. The participants in the intervention group received oral visual examination while the participants in the control group received routine awareness messages on the detrimental effects of tobacco and alcohol use. Round 1 was carried out between 1996 and 1998, and Round 2 between 1999 and 2001.
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Study participants—exclusion criteria
All Round 1 participants, including both intervention and control groups, free from oral cancer and not being bedridden (88 910 subjects), were considered for this study.
Those with missing information on height and weight (n = 932), or with extreme values of BMI [<14 (n = 866) and >38 (n = 128)] were excluded, as well as those who stated in the questionnaire to suffer from any serious illness (cancer, tuberculosis, stroke, cardiovascular disease, or asthma) (n = 11 111). Those with unknown date of death (n = 5) were also excluded. In total, 75 868 subjects were included in the mortality risk analysis and were eligible for Round 2 (Appendix 2). Between Rounds 1 and 2, 4684 persons died and 1424 subjects had missing information on weight in the Round 2 questionnaire. A total of 49 216 persons (71%) were included in the analysis of change in body weight and mortality.
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Lifestyle questionnaire
The questionnaire included demographic and socio-economic information such as age, sex, religion, education level, occupation, as well as personal medical history, tobacco use (chewing and smoking) and alcohol consumption. The anthropometric measurements (height and weight) were taken by the health workers during home visits. The weight (kg) was measured using bathroom scales accurate to 0.5 kg. The scales were kept on a flat surface with the participant stepping onto it barefoot and standing straight without holding on to anything. The height (cm) was measured using a tape while the participant stood straight on a flat surface and measurement was taken from the crown of the head to the flat surface, adjusted to the nearest centimetre.
BMI and weight change
The BMI was calculated according to the formula of weight in kilograms divided by the square of the height in metres. BMI was categorized according to the World Health Organization-STUFF cut-off points for Asian populations, <16 (severe thinness), 16–18.4 (mild and moderate thinness), 18.5–22.9 (normal range), 23–24.9 (mild overweight), 25–27.4 (pre-obese class I), 27.5–29.9 (pre-obese class II), 30–32.4 (obese class Ia), 32.5–34.9 (obese class Ib) and 35 and over (obese class II).16
However, for the risk estimates, the categorization was truncated to the right, grouping pre-obese and obese conditions into one category (BMI 25 kg/m2), and in this study, the term ‘overweight’ will correspond to a BMI between 23 to 24.9 kg/m2 and ‘obesity’ to a BMI >25 kg/m2.
Percentage of weight change was calculated as9:
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Follow-up/identification of mortality cases
Vital status was regularly and intensively assessed by field workers from municipal death registers, death records from mosques, churches and social organizations, as well as during house visits. A special liaison has been developed with the municipal death registration systems in the study area and in Trivandrum City to obtain information on mortality of all study subjects. The households of the deceased persons were visited by a health supervisor and all information regarding cause of death was collected in a verbal autopsy questionnaire after interviewing a close family member and going through the medical records.17 These questionnaires were reviewed by a medical doctor for coding the cause of death according to the International Classification of Diseases—10th Ed. This death information was matched with the study database. The start of the follow-up was the date of interview at Round 1. In the sensitivity analysis, the starting date excluded the initial 3 years of follow-up. The end of the follow-up was defined as the date of death, or 31 December 2004, whichever came first.
Outcome was all-cause mortality and cause-specific mortality (cancer, vascular diseases, diabetes and chronic respiratory diseases). Total vascular deaths included deaths due to cardiovascular diseases, cerebrovascular diseases and hypertension.
Statistical analysis
The baseline characteristics of the study population were compared according to the BMI category level.
Analyses of the mortality risks were based on multiplicative hazard function models. Relative hazards were computed using the Cox regression methods and confidence intervals were based on Wald statistics. Relative risks (RR) and 95% confidence intervals (95% CI) were estimated for the BMI categories and weight change categories. The BMI category of 18.5–22.9 kg/m2 and stable weight were used as the reference categories. Overall mortality risks were estimated both from the Round 1 interview and after exclusion of the initial 3 years of follow-up. Subsequent risks according to the socio-economical status (SES), the cause of death and the weight change were estimated after exclusion of the first 3 years. Risks were adjusted for sex, age (continuous), smoking status (never, current, past), pack-years (number of packs of cigarettes divided by the smoking duration in years), education level (illiterate, less or equal to Class VII, high school, college), occupation (manual, office worker), standard of living based on the household equipments (low class, middle class, wealthy), religion (Hindu, Muslim, Christian), tobacco chewing habits (never, current, past), alcohol habits (never, current, past), randomization group (intervention, control). Smoking in women was rare (n = 564, 1%), consequently risks among women smokers were not estimated.
Standard of living was considered the more appropriate proxy of the SES. In the present population, education level could not be used as a marker of SES as there is a lot of unemployment in spite of the higher education level.
RR were estimated on total population and on men and women separately. RR of all-cause and cause-specific deaths as compared with staying alive were calculated using the SAS PHREG procedure.18
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Results |
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The analyses included 75 868 subjects (27 243 men and 48 625 women, sex ratio M:F = 1 : 2), with a mean age of 49 years.
The baseline characteristics according to the body mass categories are shown in Table 1.
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More than 40% of the subjects were in the normal BMI range (18.5–22.9 kg/m2). Underweight subjects (BMI < 18.5 kg/m2) represented 22% of the participants and obese subjects (BMI > 30 kg/m2) were <3%. The mean BMI was 21.7 kg/m2.
BMI was inversely associated to age. Male, illiteracy, tobacco chewing and smoking, and alcohol consumption were more common in leaner study subjects (<18.5 kg/m2), and higher education or high SES were more prevalent in participants with high BMI (>23 kg/m2).
During a mean follow-up of 8 years (584 195 person-years), 4684 deaths (2521 men, 2163 women) were registered. The common causes of deaths were due to cardiovascular diseases (n = 697 men, n = 432 women), cancer (n = 342 men, n = 295 women), cerebro-vascular diseases (n = 262 men, n = 338 women) and chronic respiratory diseases (n = 142 men, n = 126 women). Other causes included diabetes (n = 123 men, n = 147 women), accidents (n = 171 men, n = 84 women), suicides (n = 158 men, n = 67 women), urinary diseases (n = 95 men, n = 92 women), infectious diseases (n = 74 men, n = 86 women), liver diseases (n = 89 men, n = 26 women), tuberculosis (n = 71 men, n = 26 women), hypertension (n = 31 men, n = 47 women), neurological diseases (n = 25 men, n = 16 women), rheumatological diseases (n = 4 men, n = 19 women) and other non-specified causes (n = 237 men, n = 362 women).
In Table 2, moderate (BMI: 16–18.4 kg/m2) and severe (BMI < 16 kg/m2) leanness was significantly associated with an increased risk of death in men and women (in men RR 1.35 (95% CI: 1.15–1.58), 1.19 (1.07–1.31); in women 1.29 (1.10–1.51), 1.17 (1.04–1.31). Overweight (BMI: 23–24.9 kg/m2) and obesity (BMI 25 kg/m2) were negatively related to mortality, although without reaching a significant level. The positive association between moderate and severe leanness and mortality was stronger among the never smokers than the current smokers. Overweight and obesity were not related to mortality among current smokers.
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Since exclusion of the initial 3 years of follow-up showed a modification in the association between BMI and mortality risk, hence subsequent risk estimates were carried out after exclusion of the first 3 years (Tables 3–5
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Table 3 shows the relationship between BMI and total mortality stratified by the SES. Low SES and low BMI categories (16–18.4 kg/m2 and
16 kg/m2) were related to an increased risk of death as compared with normal BMI (18.5–22.9 kg/m2), while BMI categories >23 kg/m2 were not associated with mortality. In the middle SES group, only moderate leanness (BMI 16–18.4 kg/m2) was positively associated with mortality as compared with normal BMI. In the high SES category, leanness was not related to death and overweight (BMI 23 kg/m2) was negatively associated with mortality risk, although not at the significance level. Table 4 shows the association between BMI and cause-specific mortality, among all the participants and the never smokers only. In all participants, there was no association between BMI and death from cancer or diabetes. Low BMI category (<16 kg/m2) was associated with a significant decreased total vascular mortality risk [RR 0.71 (95% CI 0.53–0.94)] and cardiovascular mortality risk [RR 0.68 (95% CI 0.46–0.99)] as compared with a normal BMI range (18.5–22.9 kg/m2). Inversely, moderate and severe leanness was positively associated with chronic respiratory diseases mortality. This positive association was stronger when non-smokers solely were considered. In never smokers, BMI was not related to cancer, vascular diseases and diabetes mortality.
Table 5 shows the association between weight change and total mortality risk among the 49 216 respondents of two consecutive rounds. A weight gain of 4–10% at Round 2 was related to a decreased mortality risk by 22 and 24% in men and women, respectively, as compared with a stable weight [RR in men 0.78 (95% CI 0.65–0.93), RR in women 0.76 (95% CI 0.62–0.93)]. Weight loss of >10% was associated with an increased risk of death [RR in men 1.38 (95% CI 1.07–1.79), RR in women: 1.57 (95% CI 1.23–2.00)]. Similar findings were also observed in men and women non-smokers. However, among current smokers, while weight gain was associated with decreased death risk, weight loss was no longer related to death.
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Discussion |
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In this prospective study of adult men and women from rural India, leanness was associated with an increased risk of all-cause mortality while overweight (BMI 23–24.9 kg/m2) and obesity (BMI
25 kg/m2) were not predictive risk factors. Exclusion of the first 3 years of follow-up in order to eliminate prevalent medical conditions at baseline with potential effects on BMI and mortality attenuated the risks. Low BMI (18.4 kg/m2) was a predictor of mortality from chronic respiratory diseases; in contrast, overweight and obesity were not associated with all-cause and cardio- and cerebrovascular diseases, diabetes and cancer death. Tobacco use and SES were confounding factors. Moderate weight gain over 3.5 years was associated with a decreased risk of total mortality in both non-smokers and smokers, while weight loss was a predictor of all-cause mortality in non-smokers only, with similar risk patterns in men and women. The detrimental effect of being underweight on longevity has been reported in other Asian populations such as Japan, Korea and China.19–24 Our finding, that leanness was related to increased mortality risk from chronic respiratory disease, was consistent with previous studies;25–27 the severity of the pre-existing respiratory condition may also adversely affect the patient's nutritional status. Recently, a prospective study performed in a UK population with a comparable BMI distribution showed an increased all-cause and respiratory mortality risk in the lean (BMI < 18 kg/m2).28 However, in developed countries where underweight is defined as BMI <18.5 kg/m2, these results were not consistent with four longitudinal studies, underweight being unrelated with increased mortality risk among men and women non-smokers and without any pre-existing disease.29–32
Several studies have reported the strong positive association between overweight and obesity and morbidity or mortality from cardiovascular diseases, type 2 diabetes and cancers of the breast, colon, oesophagus, endometrium and kidney.3,23,29,33–43 Even though in the present study, no association was observed between a BMI >27.5 kg/m2 and all-cause mortality, in order to compare with Western studies, we also performed analysis for BMI >30 kg/m2; obesity remained unrelated to all-cause mortality [RR: 0.87 (95% CI 0.70–1.10)]. One hypothesis for these paradoxical findings would be related to selective survival, overweight or obese persons at high mortality risk having died prematurely and those remaining being relatively healthy obese participants.44 Another hypothesis may be associated with the recent development of the reverse epidemiology theory. It has been reported that high BMI conferred better survival in specific populations: chronic heart failure patients,45 end-stage renal disease patients undergoing dialysis,46 elderly47 and hospitalized patients.48 Although the mechanisms are still unclear, it is hypothesized that well-nourished individuals are less susceptible to diseases such as vascular diseases. It is not clear either whether this theory is applicable to low-resource countries' populations. However, since high BMI is observed in the high SES group (Table 1), nutrition may play a protective role on cardiovascular diseases in this particular subgroup. Unfortunately, the association between BMI and body fat could not be addressed, as the body composition was not assessed in this cohort. For a given BMI, the body fat percentage is higher in Indians than in Caucasians,49 and body fat is a well-known risk factor of metabolic syndrome and type 2 diabetes. Yajnik50 reported that insulin resistance and type 2 diabetes were more often diagnosed in urban rather than in rural Indians where the prevalence of overweight and obesity is also higher.8,13
In addition, a collaborative study of several Asia-Pacific cohorts on BMI and cardiovascular diseases reported that in India, a BMI >25 kg/m2 had a low population attributable fraction of coronary heart disease (0.8%), haemorrhagic stroke (0.2%) and ischaemic stroke (0.9%).51 These results suggest that overweight and obesity are not strong determinants of cardio and cerebrovascular diseases in India, and that other factors are involved.
Cancer deaths in this cohort were mainly due to cancer of the head and neck, lung, breast and stomach. Other than for breast cancer, obesity is not a predictor of these cancer sites and may explain the absence of association.
Although cancer deaths have been ascertained through the population-based cancer registry and were reliable, causes of death other than cancer were based on medical charts or verbal autopsy, taking into consideration the primary cause of death solely, and not the underlying medical condition. It is likely that deaths due to underlying cardiovascular diseases or diabetes were under-reported, leading to some misclassification in the cause of death.
In Western populations, obesity is inversely associated with better education, better living standards and lower mortality risk.52,53 However, studies from Asia showed a positive association between BMI and education, better living standards and an inverse relationship with mortality.52,54 Similar findings were also observed in the present cohort.
In contrast, low BMI has been considered as a marker of under-nutrition, poor hygienic conditions, low socio-economical level and little access to medical aid facilities.11 This is especially true in rural populations from less developed countries like India.55 In low-resource countries with high mortality, underweight represents the first risk factor of global burden of non-communicable disease (14% of the total Disability-Adjusted Life Years).56 A recent national family nutrition survey reported that one-third of Indian women had a low BMI (<18.5), however 12% were overweight and 2% were obese. A discrepancy was observed according to the living standards: more underweight (49%) and less overweight (4%) among the low standard class; and inversely, few underweight (14%) and more overweight (33%) among women in the high standard class. These findings were consistent with other reports in India and Pakistan.12,57 The survey also showed differences between the place of living; in urban settings, only 12% were underweight, while 37% were overweight and in rural settings, they were 20 and 8%, respectively.58,59 Similar findings were also reported in the urban area of Mumbai.13
The relationship between weight change and mortality has been reported by several authors. Most of these studies were conducted in developed countries and the findings cannot be applied to Indian populations.60,61 The results from these studies are inconsistent. Some studies reported that both body weight loss and gain were associated with significantly increased mortality from all causes;62–64 others reported that moderate weight gain was beneficial;35,65 yet another study showed no association between weight change and cancer death.62 When using the same inclusion definition, several studies in Western populations did not report the detrimental effect of weight loss.29,30,66 The authors concluded on a confounding effect of illness and smoking on weight change and mortality. In this study, smoking status and early mortality having been accounted for, it is more likely that severe weight loss is related to a high risk of mortality, while a moderate weight gain showed a protective effect against overall mortality.
The height and body weight were carefully measured by the health workers at both the Rounds 1 and 2 surveys allowing less measurement error than self-reported height and weight. Vital status was intensively assessed by field workers, therefore, it is likely that few were lost to follow-up, and the death assessment was near completion. When the cause of death was available from hospital records or cancer registry, it was likely to be reliable. However, when no medical charts were available, the cause of death was determined using verbal autopsy as an indirect method. We also attempted to minimize the effects of pre-existing disease by excluding persons having reported severe disease at baseline, and by excluding the first 3 years of follow-up.
In conclusion, leanness (BMI < 16 kg/m2) and weight loss were strong determinants of overall death and chronic respiratory diseases in particular, while being overweight or obese in terms of WHO Asian cut-off points (BMI 23 kg/m2) had no detrimental effects on mortality in this Indian rural population. These findings confirm that underweight and weight loss have major public health implications in India, predominantly in rural areas where the leanest population is observed.8 In addition, studies from urban populations indicated that obesity and metabolic syndrome are going to pose a major public health issue.8,67,68 Malnutrition and subsequent increased mortality in rural areas on the one hand, emerging trends of obesity and associated metabolic and cardiovascular diseases in urban areas on the other hand are the main challenges in low-resource countries like India, with major implications on public health. These issues should be considered while formulating public health policies. Furthermore, there is a need of studies including both urban and rural areas to provide comprehensive information on BMI and body fat and cause-specific mortality in developing countries.
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Acknowledgements |
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This project was supported by a generous grant from the Association for International Cancer Research (AICR) St Andrews, UK, without whose assistance such a large study would not have been possible. Additional data collection is supported by the Cancer Research UK (CRUK), UK. We are grateful to the study participants and their families, the assistance of the staff of the Panchayath offices, of mortality registries and of the Trivandrum population-based cancer registry. We are also grateful to two anonymous referees and to Mrs Evelyn Bayle for editorial work on the manuscript.
Conflict of interest: None declared.
KEY MESSAGES
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